Automatic reactor model synthesis with genetic programming.

Area of Science:

• Environmental Engineering
• Computational Fluid Dynamics
• Wastewater Treatment

Background:

• Accurate hydraulic modeling is crucial for wastewater treatment plant (WWTP) process simulation.
• Traditional methods like tracer experiments are often impractical due to cost and complexity.
• Engineers frequently rely on experience, leading to potential inaccuracies.

Purpose of the Study:

• To develop a novel, automated method for constructing hydraulic reactor models for WWTPs.
• To overcome the limitations of traditional hydraulic modeling techniques.
• To provide engineers with a data-driven approach for selecting suitable reactor models.

Main Methods:

• Implementation of grammar-based genetic programming to generate hydraulic models.
• Encoding hydraulic reactor models as program trees using building blocks like continuous stirred-tank reactors.
• Utilizing only discharge measurements and influent/effluent concentrations as input data.

Main Results:

• The genetic programming approach successfully generated comparable hydraulic models from synthetic data.
• Temperature measurements from a primary clarifier were used to create a validated reactor model.
• A virtual tracer experiment on the generated model showed good agreement with on-site data.

Conclusions:

• The proposed method offers a practical and automated solution for hydraulic modeling in WWTPs.
• It reduces the need for expensive tracer studies and subjective engineering experience.
• The technique enables data-driven selection of the most appropriate reactor models for simulation.